Introducing the MECE framework

A business’s ability to identify issues and problems, remedying them in a systematic way, helps enable a business to be effective in achieving its goals.

The MECE framework is a methodology, often used by management consultancy companies, that can be used for solving complex problems by breaking down and organizing data.

We know that collecting and analyzing data is imperative in problem-solving. The MECE framework helps us ensure that this data is:

Useful in solving the problem.

Not misleading, which may result in a failure to correct the issue.

The MECE framework can help in enabling you to be better at decision making and problem-solving through these two core principles.

The MECE framework forces us to use data sets that do not confuse and include all relevant data so we don’t miss things that could be important.

What is MECE framework

The MECE framework is used extensively to target large problems. You’ll typically see it used by consultancy companies.

MECE is an acronym that stands for:

Mutually Exclusive– Items that can only fit into one category at a time

Collectively Exhaustive – Items that can fit into one of the categories.

In simple terms, this means small parts are not allowed to overlap (i.e., fit in multiple categories). The small pieces must add up to the whole.

Data is broken down into logical groups that can then be further assessed in order to resolve the business problem that is being faced.

It’s useful for targeting large complex problems by breaking them down into elements that you can appraise to help root cause analysis.

Let’s now look at each element in turn.

ME – Mutually Exclusive

Being Mutually Exclusive refers to how you break down a problem or data set into categories. The key rule is that they cannot overlap.

MECE Framework – ME Example

An example of something that overlaps could be a question such as:

Break down a group of 100 people into what meals they eat:

You could structure this into

Those that eat breakfast,

Those that eat lunch

Those that eat dinner.

This structure is not MECE; you will find individuals that overlap into two or three of the catagories. Therefore this is not mutually exclusive.

Another example would be

Break down a group of 100 people into which continent they were born in Africa, Antarctica, Australia, Eurasia (Europe + Asia), North America, South America.

As they cannot have been born in more than one continent, the data cannot overlap and is MECE.

Other examples include:

Customer age

Defined financial brackets, i.e., revenue or costs

Number of employees

Country of Birth,

CE – Collectively Exhaustive

This term simply describes how the sum of the groups selected within the ME element must equal the whole of the group.

Therefore if you look at earlier examples breaking down those that eat breakfast, lunch or dinner you may have individuals that sit in more than one category so your results will not equal your data set

In our example, where we looked at which continent you were born in: the result will be the whole of the data set.

As you gathered from the above example, MECE is all about how we group and analyze information.

Simplifying MECE framework even further

There are two further elements that you should utilize when using the MECE framework

1/ Data groups should be of the same size

For example, let’s say in framing a problem we’ve grouped our data set by location:

London

Manchester

Paris

Belgium

Germany

They are exclusive (there is no overlap). It equals all of the data, so it’s exhaustive, but let’s look at those groups again.

We’ve grouped by the city – London, Manchester, Paris AND country Belgium Germany.

Our groups are not the same size – this could lead to misleading groups and hypothesis.

Secondly, let’s look at another example:

Segmenting Income value

As an example, you could segment your customers by earnings, so you could define your customer list according to the following: